1
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Ambler R, Edmunds GL, Tan SL, Cirillo S, Pernes JI, Ruan X, Huete-Carrasco J, Wong CCW, Lu J, Ward J, Toti G, Hedges AJ, Dovedi SJ, Murphy RF, Morgan DJ, Wülfing C. PD-1 suppresses the maintenance of cell couples between cytotoxic T cells and target tumor cells within the tumor. Sci Signal 2020; 13:13/649/eaau4518. [PMID: 32934075 DOI: 10.1126/scisignal.aau4518] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The killing of tumor cells by CD8+ T cells is suppressed by the tumor microenvironment, and increased expression of inhibitory receptors, including programmed cell death protein-1 (PD-1), is associated with tumor-mediated suppression of T cells. To find cellular defects triggered by tumor exposure and associated PD-1 signaling, we established an ex vivo imaging approach to investigate the response of antigen-specific, activated effector CD8+ tumor-infiltrating lymphocytes (TILs) after interaction with target tumor cells. Although TIL-tumor cell couples readily formed, couple stability deteriorated within minutes. This was associated with impaired F-actin clearing from the center of the cellular interface, reduced Ca2+ signaling, increased TIL locomotion, and impaired tumor cell killing. The interaction of CD8+ T lymphocytes with tumor cell spheroids in vitro induced a similar phenotype, supporting a critical role of direct T cell-tumor cell contact. Diminished engagement of PD-1 within the tumor, but not acute ex vivo blockade, partially restored cell couple maintenance and killing. PD-1 thus contributes to the suppression of TIL function by inducing a state of impaired subcellular organization.
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Affiliation(s)
- Rachel Ambler
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Grace L Edmunds
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Sin Lih Tan
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Silvia Cirillo
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Jane I Pernes
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Xiongtao Ruan
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Jorge Huete-Carrasco
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Carissa C W Wong
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Jiahe Lu
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Juma Ward
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Giulia Toti
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Alan J Hedges
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK
| | - Simon J Dovedi
- R&D Oncology, AstraZeneca, Granta Park, Cambridge CB21 6GH, UK
| | - Robert F Murphy
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.,Departments of Biological Sciences, Biomedical Engineering and Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213, USA.,Freiburg Institute for Advanced Studies and Faculty of Biology, Albert Ludwig University of Freiburg, 79104 Freiburg, Germany
| | - David J Morgan
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK.
| | - Christoph Wülfing
- School of Cellular and Molecular Medicine, University of Bristol, Bristol BS8 1TD, UK.
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2
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Ruan X, Murphy RF. Evaluation of methods for generative modeling of cell and nuclear shape. Bioinformatics 2020; 35:2475-2485. [PMID: 30535313 PMCID: PMC6612826 DOI: 10.1093/bioinformatics/bty983] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Revised: 10/30/2018] [Accepted: 12/06/2018] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Cell shape provides both geometry for, and a reflection of, cell function. Numerous methods for describing and modeling cell shape have been described, but previous evaluation of these methods in terms of the accuracy of generative models has been limited. RESULTS Here we compare traditional methods and deep autoencoders to build generative models for cell shapes in terms of the accuracy with which shapes can be reconstructed from models. We evaluated the methods on different collections of 2D and 3D cell images, and found that none of the methods gave accurate reconstructions using low dimensional encodings. As expected, much higher accuracies were observed using high dimensional encodings, with outline-based methods significantly outperforming image-based autoencoders. The latter tended to encode all cells as having smooth shapes, even for high dimensions. For complex 3D cell shapes, we developed a significant improvement of a method based on the spherical harmonic transform that performs significantly better than other methods. We obtained similar results for the joint modeling of cell and nuclear shape. Finally, we evaluated the modeling of shape dynamics by interpolation in the shape space. We found that our modified method provided lower deformation energies along linear interpolation paths than other methods. This allows practical shape evolution in high dimensional shape spaces. We conclude that our improved spherical harmonic based methods are preferable for cell and nuclear shape modeling, providing better representations, higher computational efficiency and requiring fewer training images than deep learning methods. AVAILABILITY AND IMPLEMENTATION All software and data is available at http://murphylab.cbd.cmu.edu/software. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiongtao Ruan
- Computational Biology Department, School of Computer Science
| | - Robert F Murphy
- Computational Biology Department, School of Computer Science.,Departments of Biological Sciences, Biomedical Engineering, and Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA
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3
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Lee CT, Laughlin JG, Moody JB, Amaro RE, McCammon JA, Holst M, Rangamani P. An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries. Biophys J 2020; 118:1003-1008. [PMID: 32032503 PMCID: PMC7063475 DOI: 10.1016/j.bpj.2019.11.3400] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 11/08/2019] [Accepted: 11/27/2019] [Indexed: 11/16/2022] Open
Abstract
Advances in imaging methods such as electron microscopy, tomography, and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represented as a geometric mesh, which, when carefully conditioned, enables the discretization and solution of partial differential equations. In this work, we outline the steps for a naïve user to approach the Geometry-preserving Adaptive MeshER software version 2, a mesh generation code written in C++ designed to convert structural data sets to realistic geometric meshes while preserving the underlying shapes. We present two example cases: 1) mesh generation at the subcellular scale as informed by electron tomography and 2) meshing a protein with a structure from x-ray crystallography. We further demonstrate that the meshes generated by the Geometry-preserving Adaptive MeshER software are suitable for use with numerical methods. Together, this collection of libraries and tools simplifies the process of constructing realistic geometric meshes from structural biology data.
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Affiliation(s)
- Christopher T Lee
- Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, California.
| | - Justin G Laughlin
- Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, California
| | - John B Moody
- Department of Mathematics, University of California, San Diego, La Jolla, California
| | - Rommie E Amaro
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California
| | - J Andrew McCammon
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California
| | - Michael Holst
- Department of Mathematics, University of California, San Diego, La Jolla, California
| | - Padmini Rangamani
- Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, California.
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4
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Aksac A, Ozyer T, Demetrick DJ, Alhajj R. CACTUS: cancer image annotating, calibrating, testing, understanding and sharing in breast cancer histopathology. BMC Res Notes 2020; 13:13. [PMID: 31907048 PMCID: PMC6945399 DOI: 10.1186/s13104-019-4866-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 12/17/2019] [Indexed: 12/20/2022] Open
Abstract
Objective Develop CACTUS (cancer image annotating, calibrating, testing, understanding and sharing) as a novel web application for image archiving, annotation, grading, distribution, networking and evaluation. This helps pathologists to avoid unintended mistakes leading to quality assurance, teaching and evaluation in anatomical pathology. Effectiveness of the tool has been demonstrated by assessing pathologists performance in the grading of breast carcinoma and by comparing inter/intra-observer assessment of grading criteria amongst pathologists reviewing digital breast cancer images. Reproducibility has been assessed by inter-observer (kappa statistics) and intra-observer (intraclass correlation coefficient) concordance rates. Results CACTUS has been evaluated using a surgical pathology application—the assessment of breast cancer grade. We used CACTUS to present standardized images to four pathologists of differing experience. They were asked to evaluate all images to determine their assessment of Nottingham grade of a series of breast carcinoma cases. For each image, they were asked for their overall grade impression. CACTUS helps and guides pathologists to improve disease diagnosis with higher confidence and thereby reduces their workload and bias. CACTUS can be useful for both disseminating anatomical pathology images for teaching, as well as for evaluating agreement amongst pathologists or against a gold standard for evaluation or quality assurance.
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Affiliation(s)
| | - Tansel Ozyer
- TOBB University of Economics and Technology, Ankara, Turkey
| | - Douglas J Demetrick
- Laboratory Medicine, University of Calgary and Calgary Laboratory Services, Calgary, AB, Canada
| | - Reda Alhajj
- University of Calgary, Calgary, AB, Canada. .,Istanbul Medipol University, Istanbul, Turkey.
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5
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Smith K, Piccinini F, Balassa T, Koos K, Danka T, Azizpour H, Horvath P. Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays. Cell Syst 2019; 6:636-653. [PMID: 29953863 DOI: 10.1016/j.cels.2018.06.001] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 03/07/2018] [Accepted: 06/01/2018] [Indexed: 01/01/2023]
Abstract
Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.
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Affiliation(s)
- Kevin Smith
- KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvägen 3, 10044 Stockholm, Sweden; Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden
| | - Filippo Piccinini
- Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, Meldola, FC 47014, Italy
| | - Tamas Balassa
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, 6726 Szeged, Hungary
| | - Krisztian Koos
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, 6726 Szeged, Hungary
| | - Tivadar Danka
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, 6726 Szeged, Hungary
| | - Hossein Azizpour
- KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvägen 3, 10044 Stockholm, Sweden; Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden
| | - Peter Horvath
- Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári krt. 62, 6726 Szeged, Hungary; Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00014 Helsinki, Finland.
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6
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Lee J, Kolb I, Forest CR, Rozell CJ. Cell Membrane Tracking in Living Brain Tissue Using Differential Interference Contrast Microscopy. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2018; 27:1847-1861. [PMID: 29346099 PMCID: PMC5839128 DOI: 10.1109/tip.2017.2787625] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Differential interference contrast (DIC) microscopy is widely used for observing unstained biological samples that are otherwise optically transparent. Combining this optical technique with machine vision could enable the automation of many life science experiments; however, identifying relevant features under DIC is challenging. In particular, precise tracking of cell boundaries in a thick ( ) slice of tissue has not previously been accomplished. We present a novel deconvolution algorithm that achieves the state-of-the-art performance at identifying and tracking these membrane locations. Our proposed algorithm is formulated as a regularized least squares optimization that incorporates a filtering mechanism to handle organic tissue interference and a robust edge-sparsity regularizer that integrates dynamic edge tracking capabilities. As a secondary contribution, this paper also describes new community infrastructure in the form of a MATLAB toolbox for accurately simulating DIC microscopy images of in vitro brain slices. Building on existing DIC optics modeling, our simulation framework additionally contributes an accurate representation of interference from organic tissue, neuronal cell-shapes, and tissue motion due to the action of the pipette. This simulator allows us to better understand the image statistics (to improve algorithms), as well as quantitatively test cell segmentation and tracking algorithms in scenarios, where ground truth data is fully known.
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7
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Singla J, McClary KM, White KL, Alber F, Sali A, Stevens RC. Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell. Cell 2018; 173:11-19. [PMID: 29570991 PMCID: PMC6014618 DOI: 10.1016/j.cell.2018.03.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 11/25/2017] [Accepted: 03/06/2018] [Indexed: 12/25/2022]
Abstract
The construction of a predictive model of an entire eukaryotic cell that describes its dynamic structure from atomic to cellular scales is a grand challenge at the intersection of biology, chemistry, physics, and computer science. Having such a model will open new dimensions in biological research and accelerate healthcare advancements. Developing the necessary experimental and modeling methods presents abundant opportunities for a community effort to realize this goal. Here, we present a vision for creation of a spatiotemporal multi-scale model of the pancreatic β-cell, a relevant target for understanding and modulating the pathogenesis of diabetes.
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Affiliation(s)
- Jitin Singla
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kyle M McClary
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Kate L White
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA
| | - Frank Alber
- Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA; Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
| | - Andrej Sali
- California Institute for Quantitative Biosciences, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Raymond C Stevens
- Department of Biological Sciences, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA.
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8
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Ulman V, Svoboda D, Nykter M, Kozubek M, Ruusuvuori P. Virtual cell imaging: A review on simulation methods employed in image cytometry. Cytometry A 2016; 89:1057-1072. [PMID: 27922735 DOI: 10.1002/cyto.a.23031] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 07/20/2016] [Accepted: 11/14/2016] [Indexed: 02/03/2023]
Abstract
The simulations of cells and microscope images thereof have been used to facilitate the development, selection, and validation of image analysis algorithms employed in cytometry as well as for modeling and understanding cell structure and dynamics beyond what is visible in the eyepiece. The simulation approaches vary from simple parametric models of specific cell components-especially shapes of cells and cell nuclei-to learning-based synthesis and multi-stage simulation models for complex scenes that simultaneously visualize multiple object types and incorporate various properties of the imaged objects and laws of image formation. This review covers advances in artificial digital cell generation at scales ranging from particles up to tissue synthesis and microscope image simulation methods, provides examples of the use of simulated images for various purposes ranging from subcellular object detection to cell tracking, and discusses how such simulators have been validated. Finally, the future possibilities and limitations of simulation-based validation are considered. © 2016 International Society for Advancement of Cytometry.
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Affiliation(s)
- Vladimír Ulman
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - David Svoboda
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Matti Nykter
- Institute of Biosciences and Medical Technology - BioMediTech, University of Tampere, Tampere, Finland
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
| | - Pekka Ruusuvuori
- Institute of Biosciences and Medical Technology - BioMediTech, University of Tampere, Tampere, Finland.,Pori Campus, Tampere University of Technology, Pori, Finland
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9
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Li Y, Majarian TD, Naik AW, Johnson GR, Murphy RF. Point process models for localization and interdependence of punctate cellular structures. Cytometry A 2016; 89:633-43. [PMID: 27327612 DOI: 10.1002/cyto.a.22873] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2016] [Revised: 03/09/2016] [Accepted: 04/29/2016] [Indexed: 11/08/2022]
Abstract
Accurate representations of cellular organization for multiple eukaryotic cell types are required for creating predictive models of dynamic cellular function. To this end, we have previously developed the CellOrganizer platform, an open source system for generative modeling of cellular components from microscopy images. CellOrganizer models capture the inherent heterogeneity in the spatial distribution, size, and quantity of different components among a cell population. Furthermore, CellOrganizer can generate quantitatively realistic synthetic images that reflect the underlying cell population. A current focus of the project is to model the complex, interdependent nature of organelle localization. We built upon previous work on developing multiple non-parametric models of organelles or structures that show punctate patterns. The previous models described the relationships between the subcellular localization of puncta and the positions of cell and nuclear membranes and microtubules. We extend these models to consider the relationship to the endoplasmic reticulum (ER), and to consider the relationship between the positions of different puncta of the same type. Our results do not suggest that the punctate patterns we examined are dependent on ER position or inter- and intra-class proximity. With these results, we built classifiers to update previous assignments of proteins to one of 11 patterns in three distinct cell lines. Our generative models demonstrate the ability to construct statistically accurate representations of puncta localization from simple cellular markers in distinct cell types, capturing the complex phenomena of cellular structure interaction with little human input. This protocol represents a novel approach to vesicular protein annotation, a field that is often neglected in high-throughput microscopy. These results suggest that spatial point process models provide useful insight with respect to the spatial dependence between cellular structures. © 2016 International Society for Advancement of Cytometry.
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Affiliation(s)
- Ying Li
- State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, 430079, China.,Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213
| | - Timothy D Majarian
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213.,Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213
| | - Armaghan W Naik
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213
| | - Gregory R Johnson
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213
| | - Robert F Murphy
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213.,Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213.,Departments of Biomedical Engineering and Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213.,Freiburg Institute for Advanced Studies and Faculty of Biology, Albert Ludwig University of Freiburg, Albertstrasse 19, 79104 Freiburg Im Breisgau, Germany
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10
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Murphy RF. Building cell models and simulations from microscope images. Methods 2016; 96:33-39. [PMID: 26484733 PMCID: PMC4766043 DOI: 10.1016/j.ymeth.2015.10.011] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2015] [Revised: 10/15/2015] [Accepted: 10/16/2015] [Indexed: 01/13/2023] Open
Abstract
The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge.
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Affiliation(s)
- Robert F Murphy
- Computational Biology Department, Center for Bioimage Informatics, and Departments of Biological Sciences, Biomedical Engineering and Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA; Freiburg Institute for Advanced Studies and Faculty of Biology, Albert Ludwig University of Freiburg, Germany.
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11
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Abstract
Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.
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Affiliation(s)
- Heba Z Sailem
- a Department of Engineering Science , University of Oxford , Oxford , UK
| | - Sam Cooper
- b Department of Computational Systems Medicine , Imperial College, South Kensington Campus , London , UK , and.,c Division of Cancer Biology , Chester Beatty Laboratories, Institute of Cancer Research , London , UK
| | - Chris Bakal
- c Division of Cancer Biology , Chester Beatty Laboratories, Institute of Cancer Research , London , UK
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12
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Donovan RM, Tapia JJ, Sullivan DP, Faeder JR, Murphy RF, Dittrich M, Zuckerman DM. Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories. PLoS Comput Biol 2016; 12:e1004611. [PMID: 26845334 PMCID: PMC4741515 DOI: 10.1371/journal.pcbi.1004611] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Accepted: 10/16/2015] [Indexed: 12/25/2022] Open
Abstract
The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables. Stochastic simulations (simulations where randomness plays a role) of even simple biological systems are often so computationally intensive that it is impossible, in practice, to simulate them exhaustively and gather good statistics about the likelihood of different outcomes. The difficulty is compounded for the observation of rare events in these simulations; unfortunately, rare events, such as state transitions and barrier crossings, are often those of particular interest. Using the weighted ensemble (WE) method, we are able to enhance the characterization of rare events in cell biology simulations, but in such a way that the statistics for these events remain unbiased. The histogram of outcomes that WE produces has the same shape as a naive one, but the resolution of events in the tails of the histogram is greatly improved. This improved resolution in rare event statistics can be used to infer unbiased estimates of long timescale dynamics from short simulations, and we show that using a weighted ensemble can result in a reduction in total simulation time needed to sample certain events of interest in spatial, stochastic models of biological systems.
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Affiliation(s)
- Rory M. Donovan
- Joint CMU-Pitt Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Jose-Juan Tapia
- Joint CMU-Pitt Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Devin P. Sullivan
- Joint CMU-Pitt Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - James R. Faeder
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Robert F. Murphy
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Markus Dittrich
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Pittsburgh Supercomputing Center, Pittsburgh, Pennsylvania, United States of America
| | - Daniel M. Zuckerman
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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13
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14
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Seeing Is Believing: Quantifying Is Convincing: Computational Image Analysis in Biology. FOCUS ON BIO-IMAGE INFORMATICS 2016; 219:1-39. [DOI: 10.1007/978-3-319-28549-8_1] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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15
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Johnson GR, Li J, Shariff A, Rohde GK, Murphy RF. Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules. PLoS Comput Biol 2015; 11:e1004614. [PMID: 26624011 PMCID: PMC4704559 DOI: 10.1371/journal.pcbi.1004614] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 10/19/2015] [Indexed: 12/23/2022] Open
Abstract
Characterizing the spatial distribution of proteins directly from microscopy images is a difficult problem with numerous applications in cell biology (e.g. identifying motor-related proteins) and clinical research (e.g. identification of cancer biomarkers). Here we describe the design of a system that provides automated analysis of punctate protein patterns in microscope images, including quantification of their relationships to microtubules. We constructed the system using confocal immunofluorescence microscopy images from the Human Protein Atlas project for 11 punctate proteins in three cultured cell lines. These proteins have previously been characterized as being primarily located in punctate structures, but their images had all been annotated by visual examination as being simply "vesicular". We were able to show that these patterns could be distinguished from each other with high accuracy, and we were able to assign to one of these subclasses hundreds of proteins whose subcellular localization had not previously been well defined. In addition to providing these novel annotations, we built a generative approach to modeling of punctate distributions that captures the essential characteristics of the distinct patterns. Such models are expected to be valuable for representing and summarizing each pattern and for constructing systems biology simulations of cell behaviors.
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Affiliation(s)
- Gregory R. Johnson
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Jieyue Li
- Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Aabid Shariff
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Gustavo K. Rohde
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Robert F. Murphy
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Departments of Biological Sciences and Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Faculty of Biology and Freiburg Institute for Advanced Studies, Albert Ludwig University of Freiburg, Freiburg, Germany
- * E-mail:
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Johnson GR, Buck TE, Sullivan DP, Rohde GK, Murphy RF. Joint modeling of cell and nuclear shape variation. Mol Biol Cell 2015; 26:4046-56. [PMID: 26354424 PMCID: PMC4710235 DOI: 10.1091/mbc.e15-06-0370] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Accepted: 08/28/2015] [Indexed: 11/11/2022] Open
Abstract
Modeling cell shape variation is critical to our understanding of cell biology. Previous work has demonstrated the utility of nonrigid image registration methods for the construction of nonparametric nuclear shape models in which pairwise deformation distances are measured between all shapes and are embedded into a low-dimensional shape space. Using these methods, we explore the relationship between cell shape and nuclear shape. We find that these are frequently dependent on each other and use this as the motivation for the development of combined cell and nuclear shape space models, extending nonparametric cell representations to multiple-component three-dimensional cellular shapes and identifying modes of joint shape variation. We learn a first-order dynamics model to predict cell and nuclear shapes, given shapes at a previous time point. We use this to determine the effects of endogenous protein tags or drugs on the shape dynamics of cell lines and show that tagged C1QBP reduces the correlation between cell and nuclear shape. To reduce the computational cost of learning these models, we demonstrate the ability to reconstruct shape spaces using a fraction of computed pairwise distances. The open-source tools provide a powerful basis for future studies of the molecular basis of cell organization.
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Affiliation(s)
- Gregory R Johnson
- Computational Biology Department and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Taraz E Buck
- Computational Biology Department and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Devin P Sullivan
- Computational Biology Department and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Gustavo K Rohde
- Computational Biology Department and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213 Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Robert F Murphy
- Computational Biology Department and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213 Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 Departments of Biological Sciences and Machine Learning, Carnegie Mellon University, Pittsburgh, PA 15213 Freiburg Institute for Advanced Studies and Faculty of Biology, Albert Ludwig University of Freiburg, 79104 Freiburg im Breisgau, Germany
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Chylek LA, Harris LA, Faeder JR, Hlavacek WS. Modeling for (physical) biologists: an introduction to the rule-based approach. Phys Biol 2015; 12:045007. [PMID: 26178138 PMCID: PMC4526164 DOI: 10.1088/1478-3975/12/4/045007] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions.
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Affiliation(s)
- Lily A Chylek
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, USA
- Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
| | - Leonard A Harris
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37212, USA
| | - James R Faeder
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260, USA
| | - William S Hlavacek
- Theoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
- New Mexico Consortium, Los Alamos, NM 87544, USA
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Medyukhina A, Timme S, Mokhtari Z, Figge MT. Image-based systems biology of infection. Cytometry A 2015; 87:462-70. [PMID: 25641512 DOI: 10.1002/cyto.a.22638] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 01/05/2015] [Accepted: 01/07/2015] [Indexed: 12/21/2022]
Abstract
The successful treatment of infectious diseases requires interdisciplinary studies of all aspects of infection processes. The overarching combination of experimental research and theoretical analysis in a systems biology approach can unravel mechanisms of complex interactions between pathogens and the human immune system. Taking into account spatial information is especially important in the context of infection, since the migratory behavior and spatial interactions of cells are often decisive for the outcome of the immune response. Spatial information is provided by image and video data that are acquired in microscopy experiments and that are at the heart of an image-based systems biology approach. This review demonstrates how image-based systems biology improves our understanding of infection processes. We discuss the three main steps of this approach--imaging, quantitative characterization, and modeling--and consider the application of these steps in the context of studying infection processes. After summarizing the most relevant microscopy and image analysis approaches, we discuss ways to quantify infection processes, and address a number of modeling techniques that exploit image-derived data to simulate host-pathogen interactions in silico.
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Affiliation(s)
- Anna Medyukhina
- Applied Systems Biology, HKI-Center for Systems Biology of Infection, Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute (HKI), Jena, Germany
| | - Sandra Timme
- Applied Systems Biology, HKI-Center for Systems Biology of Infection, Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute (HKI), Jena, Germany.,Applied Systems Biology, Friedrich Schiller University, Jena, Germany
| | - Zeinab Mokhtari
- Applied Systems Biology, HKI-Center for Systems Biology of Infection, Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute (HKI), Jena, Germany.,Applied Systems Biology, Friedrich Schiller University, Jena, Germany
| | - Marc Thilo Figge
- Applied Systems Biology, HKI-Center for Systems Biology of Infection, Leibniz-Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute (HKI), Jena, Germany.,Applied Systems Biology, Friedrich Schiller University, Jena, Germany
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Svoboda D. Next step toward the automation of screening for cervical cancer. Cytometry A 2015; 87:195-6. [PMID: 25572635 DOI: 10.1002/cyto.a.22564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2014] [Accepted: 08/19/2014] [Indexed: 11/10/2022]
Affiliation(s)
- David Svoboda
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic
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Roybal KT, Sinai P, Verkade P, Murphy RF, Wülfing C. The actin-driven spatiotemporal organization of T-cell signaling at the system scale. Immunol Rev 2014; 256:133-47. [PMID: 24117818 DOI: 10.1111/imr.12103] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
T cells are activated through interaction with antigen-presenting cells (APCs). During activation, receptors and signaling intermediates accumulate in diverse spatiotemporal distributions. These distributions control the probability of signaling interactions and thus govern information flow through the signaling system. Spatiotemporally resolved system-scale investigation of signaling can extract the regulatory information thus encoded, allowing unique insight into the control of T-cell function. Substantial technical challenges exist, and these are briefly discussed herein. While much of the work assessing T-cell spatiotemporal organization uses planar APC substitutes, we focus here on B-cell APCs with often stark differences. Spatiotemporal signaling distributions are driven by cell biologically distinct structures, a large protein assembly at the interface center, a large invagination, the actin-supported interface periphery as extended by smaller individual lamella, and a newly discovered whole-interface actin-driven lamellum. The more than 60 elements of T-cell activation studied to date are dynamically distributed between these structures, generating a complex organization of the signaling system. Signal initiation and core signaling prefer the interface center, while signal amplification is localized in the transient lamellum. Actin dynamics control signaling distributions through regulation of the underlying structures and drive a highly undulating T-cell/APC interface that imposes substantial constraints on T-cell organization. We suggest that the regulation of actin dynamics, by controlling signaling distributions and membrane topology, is an important rheostat of T-cell signaling.
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Affiliation(s)
- Kole T Roybal
- Department of Immunology, UT Southwestern Medical Center, Dallas, TX, USA
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Peng H, Bria A, Zhou Z, Iannello G, Long F. Extensible visualization and analysis for multidimensional images using Vaa3D. Nat Protoc 2014; 9:193-208. [PMID: 24385149 DOI: 10.1038/nprot.2014.011] [Citation(s) in RCA: 169] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Open-Source 3D Visualization-Assisted Analysis (Vaa3D) is a software platform for the visualization and analysis of large-scale multidimensional images. In this protocol we describe how to use several popular features of Vaa3D, including (i) multidimensional image visualization, (ii) 3D image object generation and quantitative measurement, (iii) 3D image comparison, fusion and management, (iv) visualization of heterogeneous images and respective surface objects and (v) extension of Vaa3D functions using its plug-in interface. We also briefly demonstrate how to integrate these functions for complicated applications of microscopic image visualization and quantitative analysis using three exemplar pipelines, including an automated pipeline for image filtering, segmentation and surface generation; an automated pipeline for 3D image stitching; and an automated pipeline for neuron morphology reconstruction, quantification and comparison. Once a user is familiar with Vaa3D, visualization usually runs in real time and analysis takes less than a few minutes for a simple data set.
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Affiliation(s)
- Hanchuan Peng
- 1] Allen Institute for Brain Sciences, Seattle, Washington, USA. [2] Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA
| | - Alessandro Bria
- 1] Integrated Research Centre, University Campus Bio-Medico of Rome, Rome, Italy. [2] Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, Cassino, Italy
| | - Zhi Zhou
- Allen Institute for Brain Sciences, Seattle, Washington, USA
| | - Giulio Iannello
- Integrated Research Centre, University Campus Bio-Medico of Rome, Rome, Italy
| | - Fuhui Long
- 1] Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, USA. [2] BioImage, LLC, Bellevue, Washington, USA
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